rmarkdown
.mdhtml.Rmdrmarkdown integrates:– a documentantion language (.md)
with:
– a programming language (R)
single document to integrate data analysis with textual representations, linking data, code, and text
Rmarkdown & reproducibilityComputational science has led to exciting new developments:
Increasing computational complexity of analyses:
has exposed limitations in our ability to evaluate published findings.
Even basic analyses difficult to describe
Errors more easily introduced into long analysis pipelines
Knowledge transfer is inhibited
Results are difficult to replicate or reproduce
Complicated analyses cannot be trusted
Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.
ultimately does not address the key question:
can we trust these results?
evdence needs:
rmarkdown can integrate tools, processes and outputs into evidence streams that are easily shareable
at all stages of scientific process
empower your code and data
To help solve these problems, we make a number of suggestions including providing blog posts or videos to explain new methods in less technical terms, encouraging reproducibility and code sharing, making wiki-style pages summarising the literature on popular methods, more careful consideration and testing of whether a method is appropriate for a given question/data set, increased collaboration, and a shift from publishing purely novel methods to publishing improvements to existing methods and ways of detecting biases or testing model fit. Many of these points are applicable across methods in ecology and evolution, not just phylogenetic comparative methods.
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`this text will appear as code` inline
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a <- 10 the value of parameter *a* is 10
the value of parameter a is 10


<img src="assets/cheat.png" width="200px" />
Table Header | Second Header
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Cell 3 | Cell 4
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[Download R](http://www.r-project.org/)
[RStudio](http://www.rstudio.com/)
.md resourcesgithub.io websites: eg Andy South’s blog
Reproducible Research coursera MOOC
R code chunks can be used as a means render R output into documents or to simply display code for illustration
for more details see http://yihui.name/knitr/
knitr::opts_chunk$set(echo = TRUE, warning = F, message = F)knitr::kable() tablesrequire(knitr)
data(airquality)
kable(head(airquality), caption = "New York Air Quality Measurements")| Ozone | Solar.R | Wind | Temp | Month | Day |
|---|---|---|---|---|---|
| 41 | 190 | 7.4 | 67 | 5 | 1 |
| 36 | 118 | 8.0 | 72 | 5 | 2 |
| 12 | 149 | 12.6 | 74 | 5 | 3 |
| 18 | 313 | 11.5 | 62 | 5 | 4 |
| NA | NA | 14.3 | 56 | 5 | 5 |
| 28 | NA | 14.9 | 66 | 5 | 6 |
DT::datatable() tablesrequire(DT)
data(airquality)
datatable(airquality, caption = "New York Air Quality Measurements")library(plotly)
set.seed(100)
d <- diamonds[sample(nrow(diamonds), 1000), ]
p <- ggplot(data = d, aes(x = carat, y = price)) +
geom_point(aes(text = paste("Clarity:", clarity)), size = 1) +
geom_smooth(aes(colour = cut, fill = cut)) + facet_wrap(~ cut)
ggplotly(p)
create your first
.Rmd!
datasets packagedata(package = .packages(all.available = TRUE))